


from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
				import tensorflow as tf

				from ..tf_utils import stable_softmax


if is_torch_available():
				import torch


lowerCAmelCase__			:      Optional[int]								=logging.get_logger(__name__)







@add_end_docstrings(
    __SCREAMING_SNAKE_CASE	,       r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """	,       )
class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if self.framework == "tf":
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()
										elif self.framework == "pt":
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.nonzero(input_ids == self.tokenizer.mask_token_id			,						as_tuple=lowerCAmelCase__       )
										else:
													raise ValueError('Unsupported framework'       )
										return masked_index






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.get_masked_index(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.prod(masked_index.shape       )
										if numel < 1:
													raise PipelineException(
													    'fill-mask'			,						self.model.base_model_prefix			,						F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
													for model_input in model_inputs:
																self._ensure_exactly_one_mask_token(model_input['input_ids'][0]       )
										else:
													for input_ids in model_inputs["input_ids"]:
																self._ensure_exactly_one_mask_token(lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										if return_tensors is None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.framework
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__       )
										self.ensure_exactly_one_mask_token(lowerCAmelCase__       )
										return model_inputs






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.model(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					model_inputs['input_ids']
										return model_outputs






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=5			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										if target_ids is not None and target_ids.shape[0] < top_k:
													SCREAMING_SNAKE_CASE_		:     Dict					=					target_ids.shape[0]
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model_outputs['input_ids'][0]
										SCREAMING_SNAKE_CASE_		:     Dict					=					model_outputs['logits']

										if self.framework == "tf":
													SCREAMING_SNAKE_CASE_		:     Any					=					tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()[:, 0]

													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					outputs.numpy()

													SCREAMING_SNAKE_CASE_		:     str					=					outputs[0, masked_index, :]
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					stable_softmax(lowerCAmelCase__			,						axis=-1       )
													if target_ids is not None:
																SCREAMING_SNAKE_CASE_		:     Any					=					tf.gather_nd(tf.squeeze(lowerCAmelCase__			,						0       )			,						target_ids.reshape(-1			,						1       )       )
																SCREAMING_SNAKE_CASE_		:     str					=					tf.expand_dims(lowerCAmelCase__			,						0       )

													SCREAMING_SNAKE_CASE_		:     List[Any]					=					tf.math.top_k(lowerCAmelCase__			,						k=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					topk.values.numpy(), topk.indices.numpy()
										else:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.nonzero(input_ids == self.tokenizer.mask_token_id			,						as_tuple=lowerCAmelCase__       ).squeeze(-1       )
													# Fill mask pipeline supports only one ${mask_token} per sample

													SCREAMING_SNAKE_CASE_		:     int					=					outputs[0, masked_index, :]
													SCREAMING_SNAKE_CASE_		:     Any					=					logits.softmax(dim=-1       )
													if target_ids is not None:
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					probs[..., target_ids]

													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					probs.topk(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					[]
										SCREAMING_SNAKE_CASE_		:     int					=					values.shape[0] == 1
										for i, (_values, _predictions) in enumerate(zip(values.tolist()			,						predictions.tolist()       )       ):
													SCREAMING_SNAKE_CASE_		:     Dict					=					[]
													for v, p in zip(_values			,						_predictions       ):
																# Copy is important since we're going to modify this array in place
																SCREAMING_SNAKE_CASE_		:     str					=					input_ids.numpy().copy()
																if target_ids is not None:
																			SCREAMING_SNAKE_CASE_		:     Tuple					=					target_ids[p].tolist()

																SCREAMING_SNAKE_CASE_		:     Tuple					=					p
																# Filter padding out:
																SCREAMING_SNAKE_CASE_		:     int					=					tokens[np.where(tokens != self.tokenizer.pad_token_id       )]
																# Originally we skip special tokens to give readable output.
																# For multi masks though, the other [MASK] would be removed otherwise
																# making the output look odd, so we add them back
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer.decode(lowerCAmelCase__			,						skip_special_tokens=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Any					=					{'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]       ), 'sequence': sequence}
																row.append(lowerCAmelCase__       )
													result.append(lowerCAmelCase__       )
										if single_mask:
													return result[0]
										return result






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[targets]
										try:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.tokenizer.get_vocab()
										except Exception:
													SCREAMING_SNAKE_CASE_		:     str					=					{}
										SCREAMING_SNAKE_CASE_		:     List[str]					=					[]
										for target in targets:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					vocab.get(lowerCAmelCase__			,						lowerCAmelCase__       )
													if id_ is None:
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(
																    lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						max_length=1			,						truncation=lowerCAmelCase__			,						)['input_ids']
																if len(lowerCAmelCase__       ) == 0:
																			logger.warning(
																			    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																			    'We cannot replace it with anything meaningful, ignoring it'       )
																			continue
																SCREAMING_SNAKE_CASE_		:     List[str]					=					input_ids[0]
																# XXX: If users encounter this pass
																# it becomes pretty slow, so let's make sure
																# The warning enables them to fix the input to
																# get faster performance.
																logger.warning(
																    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																    F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_       )}`.'''       )
													target_ids.append(id_       )
										SCREAMING_SNAKE_CASE_		:     int					=					list(set(lowerCAmelCase__       )       )
										if len(lowerCAmelCase__       ) == 0:
													raise ValueError('At least one target must be provided when passed.'       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array(lowerCAmelCase__       )
										return target_ids






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					{}

										if targets is not None:
													SCREAMING_SNAKE_CASE_		:     str					=					self.get_target_ids(lowerCAmelCase__			,						lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					target_ids

										if top_k is not None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					top_k

										if self.tokenizer.mask_token_id is None:
													raise PipelineException(
													    'fill-mask'			,						self.model.base_model_prefix			,						'The tokenizer does not define a `mask_token`.'       )
										return {}, {}, postprocess_params






							def __call__(      self			,						lowerCAmelCase__			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					super().__call__(lowerCAmelCase__			,						**lowerCAmelCase__       )
										if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) and len(lowerCAmelCase__       ) == 1:
													return outputs[0]
										return outputs






import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings